In the conventional static and exclusive spectrum allocation paradigm, there is not much desirable radio frequency (RF) spectrum left to meet the ever-increasing demand from the existing and upcoming wireless services. It has been found that a significant amount of RF spectrum is underutilized in the space, time, and frequency dimensions.
In the past decade, several spectrum-sharing models have been investigated. Depending on the degree of sharing, the various spectrum-sharing approaches fall into exclusive spectrum use, static spectrum sharing, dynamic spectrum sharing, and pure spectrum sharing categories. A key challenge for the spectrum-sharing models is defining and enforcing spectrum-access rights under unknown RF-environment conditions and spectrum-access scenarios. Defining spectrum-sharing constraints to ensure minimum performance under the worst-case propagation conditions severely limits the opportunities to exploit the underutilized spectrum.
The dynamic spectrum sharing approaches have been evolving since the past decade. Depending on the degree of sharing, the various spectrum sharing approaches fall into exclusive spectrum use, static spectrum sharing, dynamic spectrum sharing, and pure spectrum sharing categories. Dynamic spectrum sharing differs from pure spectrum sharing in the sense that under pure spectrum sharing all services have equal spectrum-access priority. A previous work classified spectrum sharing approaches into open sharing model, dynamic exclusive use model, and hierarchical access model. The hierarchical access model could be further categorized into spectrum underlay model, non-prioritized spectrum overlay model, and prioritized spectrum overlay model. Spectrum underlay model imposes tight constraints on secondary spectrum-access in order to protect the spectrum-access rights of the incumbents. Under non-prioritized spectrum overlay model, a secondary spectrum-access is granted on a first come, first served basis while ensuring non-harmful interference to the receivers of the incumbent services. Under prioritized spectrum overlay model, certain services are assigned priority access privileges and the secondary access by these services is protected. Other non-prioritized secondary spectrum accesses are required to vacate if a priority user wishes to access spectrum. The proposed 3.5 GHz Citizens Broadband Radio Service (CBRS) is an example of prioritized spectrum overlay model.
In terms of articulating the spectrum access rights, the spectrum sharing mechanisms primarily resort to statically or dynamically defining a spatio-temporal boundary along with a fixed set of constraints. In this regard, the case study of dynamic spectrum sharing in UHF bands has brought out several technical, regulatory, and business difficulties.
In November 2008, Federal Communications Commission (FCC) released a Notice of Proposed Rule Making (NPRM) to allow the unlicensed radios to operate in the TV bands without causing harmful interference to the incumbent services. The Opportunistic Spectrum Access (OSA) of the unused UHF bands received a wide commercial interest for several potential wireless services; However, the performance estimation studies of OSA have revealed that the amount of the implied available spectrum is very limited to meet the increasing demand for RF spectrum. Moreover, the secondary users cannot ensure desired quality of service necessary for the business cases due to the secondary rights for accessing the spectrum. On the other hand, incumbents do not have any incentive for sharing the spectrum. Furthermore, the secondary access to the spectrum is very hard to regulate. Considering interference aggregation effects, dynamic nature of propagation conditions, and dynamic spectrum-access scenarios, the primary owners of the spectrum need a way to confirm that their receivers are not subjected to harmful interference and the service experience is not degraded. This requires the ability to reliably estimate the interference margin at the receivers and accordingly infer the maximum transmit-power at the secondary transmitter positions. Furthermore, the behavior of software defined radio devices could be altered with software changes and thus the service is exposed to attacks from the secondary users of the spectrum. In order to ensure protection of the spectrum rights, the spectrum-access constraints need to be enforceable.
We observe that the decisions for exercising spectrum-access in case of OSA are based on detection of primary transmitter signal using a certain specified radio sensitivity. In this case, the decision for spectrum-access is binary in nature. This gives rise to ‘not enough spectrum for secondary usage’ if the policy for shared spectrum-access is conservative and ‘no guarantee for ensuring service quality’ if the shared spectrum-access policy is aggressive. The binary nature of the spectrum-access decision cannot protect the spectrum rights of incumbents and requires the spectrum-access policy to be increasingly conservative to guard against interference aggregation. Therefore, when multiple secondary transmitters exercise spectrum-access, we need to quantitatively articulate the spectrum-access rights. This helps maximizing a spectrum-access opportunity without causing harmful interference. If technical and regulatory problems are solved, more and more incumbents will have an incentive to share the spatially, temporally, and spectrally unexploited spectrum.
FIG. 12 illustrates the need for a methodology to characterize and quantify the use of spectrum under dynamic spectrum sharing paradigm with the aid of a question-map. The question-map enumerates the quantitative decisions involved in the process of investigating the weaknesses of a spectrum sharing mechanism, comparing various algorithms and architectures for recovery and exploitation of the spectrum, and optimizing the spectrum sharing opportunities.
Traditionally the performance of spectrum recovery is measured in terms of the throughput for the secondary users and outage probability. The performance of detection of spectrum holes is also captured in terms of probability of missed detection and false positives. However, this characterization of the performance is in the context of spectrum sharing constraints defined by a certain spectrum sharing model or in terms of system-level objectives. In order to maximize the use of spectrum, we need a methodology that can characterize the performance of the recovery and exploitation of the underutilized spectrum in the space, time, and frequency dimensions.
The previous methodologies to define the use of spectrum and quantify its efficiency are based on the static spectrum assignment paradigm and are not suitable for the dynamic spectrum sharing paradigm. ITU defined spectrum utilization factor as product of the frequency bandwidth, geometric space, and the time denied to other potential users. However, spectrum utilization factor does not represent actual usage. For example, if a licensed user does not perform any transmissions, the spectrum is still considered to be used. It also cannot quantify the use of spectrum under spatial overlap of wireless services. The IEEE 1900.5.2 draft standard captures spectrum usage in terms of transceiver-model parameters and applies standard methods for ensuring compatibility between the spectrum sharing networks. Thus, the approach helps to ensure compatibility; however, it cannot characterize and quantify the use of spectrum and the performance of spectrum management functions.